1. Field of the Invention
The present invention relates to an information processing apparatus and method for training a classifier.
2. Description of the Related Art
Conventionally, study of object recognition has been conducted on object categorization that is of recognizing unseen-before objects of a given category and assigning the correct category label. Further, a study has been conducted on estimating, by using model information of the object, the position and the orientation of the object in addition to object categorization. These techniques are applied, for example, to recognition of positions and orientations of parts for causing a robot to perform a high-level assembly operation.
A method (implicit-shape-model) for associating a codebook of features extracted from training images with detected features and estimating the position of the object center by probabilistic voting is discussed in B. Leibe's “Robust Object Detection with Interleaved Categorization and Segmentation”, IJCV Special Issue on Learning for Vision for learning, August 2007. According to the method, not only the object category, but also the position of the object can be estimated.
In the method discussed in Japanese Patent Application Laid-Open No. 2008-257649, feature points are extracted from an input image to calculate feature amounts thereof, and a feature point in the input image having a similar feature amount to that of a feature point in the training image is set to be a corresponding point. Then, for each corresponding point in the input image, based on the feature amount (including the position information) of the corresponding feature point in the training image, a vote is casted for a reference point so that the object category is classified and the position thereof is also estimated.
To increase the recognition accuracy, it is important to appropriately select feature points and feature amounts of the feature points. In place of the feature points, partial regions (hereinafter referred to as local regions) of an object may be used. Japanese Patent Application Laid-Open No. 2011-216087 discusses a technique for, in each node in a tree structure created in a training process, extracting feature amounts from a region (foreground region) where a recognition target object exists so that the object can be recognized in a state where the background frequently changes.
Further, Japanese Patent No. 4852086 discusses a technique for, in an important part for recognition, closely arranging local regions to be used for recognition processing by using a loss function for obtaining a loss value for the degree of similarity or dissimilarity between an input pattern and a standard pattern.
When an object is to be classified, the object is classified based on training images. Accordingly, to improve the object recognition accuracy, it is important to accurately train a classifier and select important training images. Unfortunately, conventional techniques do not provide enough accuracy to train a classifier and select important training images, and thus the accuracy in classifying an object is not adequate, either.